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Novel global robust exponential stability criterion for uncertain BAM neural networks with time-varying delays

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  • Sheng, Li
  • Yang, Huizhong

Abstract

In this paper, the global robust exponential stability for a class of delayed BAM neural networks with norm-bounded uncertainty is studied. Some less conservative conditions are presented for the global exponential stability of BAM neural networks with time-varying delays by constructing a new class of Lyapunov functionals combined with free-weighting matrices. This novel approach, based on the linear matrix inequality (LMI) technique, removes some existing restrictions on the system’s parameters, and the derived conditions are easy to verify via the LMI toolbox. Comparisons between our results and previous results admit that our results establish a new set of stability criteria for delayed BAM neural networks.

Suggested Citation

  • Sheng, Li & Yang, Huizhong, 2009. "Novel global robust exponential stability criterion for uncertain BAM neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 40(5), pages 2102-2113.
    • Handle: RePEc:eee:chsofr:v:40:y:2009:i:5:p:2102-2113
    DOI: 10.1016/j.chaos.2007.09.098
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    References listed on IDEAS

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    7. Huang, Xia & Cao, Jinde & Huang, De-Shuang, 2005. "LMI-based approach for delay-dependent exponential stability analysis of BAM neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 24(3), pages 885-898.
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    Cited by:

    1. Li, Liangliang & Jian, Jigui, 2015. "Exponential p-convergence analysis for stochastic BAM neural networks with time-varying and infinite distributed delays," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 860-873.
    2. Wang, Huiwei & Song, Qiankun & Duan, Chengjun, 2010. "LMI criteria on exponential stability of BAM neural networks with both time-varying delays and general activation functions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(4), pages 837-850.

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